A decomposition-based constraint optimization approach for statically scheduling task graphs with communication delays to multiprocessors

We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders decomposition, our technique solves relaxed versions of the problem and iteratively learns constraints to prune the solution space. Typical formula...

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Veröffentlicht in:Proceedings of the conference on Design, automation and test in Europe S. 57 - 62
Hauptverfasser: Satish, Nadathur, Ravindran, Kaushik, Keutzer, Kurt
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: San Jose, CA, USA EDA Consortium 16.04.2007
Schriftenreihe:ACM Conferences
Schlagworte:
Computing methodologies
Computing methodologies > Artificial intelligence
Computing methodologies > Artificial intelligence > Search methodologies
Computing methodologies > Artificial intelligence > Search methodologies > Heuristic function construction
Theory of computation
Theory of computation > Design and analysis of algorithms
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis
Theory of computation > Design and analysis of algorithms > Approximation algorithms analysis > Scheduling algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms
Theory of computation > Design and analysis of algorithms > Online algorithms > Online learning algorithms > Scheduling algorithms
Theory of computation > Theory and algorithms for application domains
Theory of computation > Theory and algorithms for application domains > Machine learning theory
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Reinforcement learning > Sequential decision making
ISBN:3981080122, 9783981080124
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Beschreibung
Zusammenfassung:We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders decomposition, our technique solves relaxed versions of the problem and iteratively learns constraints to prune the solution space. Typical formulations suffer prohibitive run times even on medium-sized problems with less than 30 tasks. Our decomposition strategy enhances constraint optimization to robustly handle instances with over 100 tasks. Moreover, the extensibility of constraint formulations permits realistic application and resource constraints, which is a limitation of common heuristic methods for scheduling. The inherent extensibility, coupled with improved run times from a decomposition strategy, posit constraint optimization as a powerful tool for resource constrained scheduling and multiprocessor design space exploration.
ISBN:3981080122
9783981080124
DOI:10.5555/1266366.1266381